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- Tasks: Build and deploy cutting-edge ML models using Python and popular frameworks.
- Company: Join a forward-thinking tech company that values diversity and innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Inclusive environment encouraging applicants from all backgrounds.
- Why this job: Make an impact in the AI field while working with the latest technologies.
- Qualifications: Strong Python skills and experience with ML frameworks like TensorFlow or PyTorch.
The predicted salary is between 60000 - 80000 £ per year.
Strong proficiency in Python, familiarity with popular libraries, and the standard ML Python stack.
Knowledge of frameworks for building complex ML models (e.g. TensorFlow, PyTorch, or Keras).
Ability to preprocess and feature engineer data (cleaning, transformation, feature extraction).
Ability to deploy and serve models in production-ready environments (requiring knowledge of containerisation, orchestration, and model serving platforms - Docker, Kubernetes, TensorFlow, etc).
Familiar with model interpretability and explainability and techniques to interpret and explain model results (e.g. SHAP, LIME).
Experience in Machine Learning.
Familiarity with ML Ops — not just building models but deploying them as a Machine Learning Operational Platform.
Azure Databricks expertise is a must have.
Knowledge of Spark for big data processing.
Knowledge of common deep learning approaches.
Knowledge of ML Flow for lifecycle management (Bonus).
We welcome applications from all individuals, regardless of background or identity, and we encourage candidates who may not meet every listed requirement to still apply.
If you require any adjustments or support during the recruitment process, please let us know and we will work with you to ensure a fair and accessible experience.
ML Engineer in England employer: LA International
Contact Detail:
LA International Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land ML Engineer in England
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with ML engineers on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those involving Python, TensorFlow, or PyTorch. Having tangible examples of your work can really set you apart during interviews.
✨Tip Number 3
Prepare for technical interviews by brushing up on your ML concepts and coding skills. Practice common algorithms and frameworks, and be ready to discuss your approach to model deployment and interpretability.
✨Tip Number 4
Don’t forget to apply through our website! We’re always looking for talented individuals, and applying directly can sometimes give you an edge. Plus, we’re here to support you throughout the process!
We think you need these skills to ace ML Engineer in England
Some tips for your application 🫡
Show Off Your Skills: Make sure to highlight your strong proficiency in Python and any experience you have with popular ML libraries. We want to see how you've used frameworks like TensorFlow or PyTorch in your projects, so don’t hold back!
Be Specific About Your Experience: When detailing your past roles, focus on your experience with data preprocessing and feature engineering. We love to see examples of how you've cleaned and transformed data, so share those stories with us!
Talk About Deployment: Don’t forget to mention your experience with deploying models in production-ready environments. If you’ve worked with Docker or Kubernetes, let us know how you’ve used these tools to serve your models effectively.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at LA International
✨Know Your Tech Stack
Make sure you’re well-versed in Python and the libraries mentioned in the job description. Brush up on TensorFlow, PyTorch, and Keras, as well as your knowledge of Docker and Kubernetes. Being able to discuss your experience with these tools confidently will show that you're ready for the role.
✨Showcase Your Data Skills
Prepare to talk about your experience with data preprocessing and feature engineering. Have specific examples ready where you've cleaned, transformed, or extracted features from datasets. This will demonstrate your hands-on skills and understanding of the ML pipeline.
✨Discuss Model Deployment
Be ready to explain how you've deployed models in production environments. Discuss any experience you have with containerisation and orchestration, and be prepared to share challenges you faced and how you overcame them. This shows you understand the full lifecycle of machine learning projects.
✨Understand Interpretability Techniques
Familiarise yourself with model interpretability techniques like SHAP and LIME. Be prepared to discuss why they are important and how you’ve used them in past projects. This will highlight your ability to not only build models but also to explain their results effectively.